Exploring Chinese word embedding with similar context and reinforcement learning

Chinese word embedding has attracted considerable attention in the field of natural language processing. Existing methods model the relation between target and neighbouring contextual words. However, with the phenomenon of irrelevant neighbouring words in Chinese, these methods are limited in capturing and understanding the semantics of Chinese words. In this study, we designed sc2vec to explore Chinese word embeddings by proposing a similar context to reduce the influence of the above problem and comprehend relevant semantics of Chinese words. Meanwhile, to enhance the learning architecture, sc2vec was modelled with reinforcement learning to generate high-quality Chinese word embeddings, regarding continuous bag-of-words and skip-gram models as two actions of an agent over a corpus. The results on word analogy, word similarity, named entity recognition, and text classification tasks demonstrate that the proposed model outperforms most state-of-the-art approaches.